Real Time Risk Loan.pdf

Front cover

Risk Scoring for a Loan Application on IBM System z Running IBM SPSS Real-Time Analytics Using IBM SPSS Modeler for Analytics modeling Configuring risk assessment in SPSS Decision Management Real-time scoring using a System z host

Mike Ebbers Keith Doan Andrew Flatt

ibm.com/redbooks

International Technical Support Organization Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics October 2013

SG24-8153-00

Note: Before using this information and the product it supports, read the information in “Notices” on page v.

First Edition (October 2013) This edition applies to Version 10 of IBM DB2 for z/OS, Version 4.1 of IBM CICS, and Version 15 of SPSS.

© Copyright International Business Machines Corporation 2013. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp.

Contents Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .v Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Chapter 1. Proof of Technology overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Planning the Proof of Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Tasks to plan and execute the PoT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Several weeks before the PoT delivery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 About one week before the PoT delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 As soon as you have access to the System z system . . . . . . . . . . . . . . . . . . . . . . 1.2.4 Just before PoT delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Flow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Actual implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Modules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Handouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Presentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Products used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 3 3 3 3 4 4 5 6 7 8 8 8

Chapter 2. PoT technical overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Preparing the System z environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 z/OS LPAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.2 z/Linux system: platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.3 z/Linux system: analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Preparing the clients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 Client software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3 Final checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.1 Run the scoring transaction to test setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Chapter 3. Preparations before you start . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Important reading guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Worksheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 General system information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 20 21 22

Chapter 4. Worksheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.1 General system information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Chapter 5. Analytics modeling with IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Background reading: Modeling and IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Key questions for a modeling project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Introduction to IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 IBM SPSS Modeler benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 What this lab is about . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 What will you learn? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

© Copyright IBM Corp. 2013. All rights reserved.

25 26 26 27 27 29 29

iii

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5.2.2 Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Building a predictive model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Starting SPSS Modeler Workspace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Connecting to data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Building a model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Deploying a model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Creating a scoring workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Deploying scoring workflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29 29 29 31 38 45 45 49 52

Chapter 6. Configure the risk assessment in SPSS Decision Management . . . . . . . . 6.1 Introduction to SPSS Decision Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Create your SPSS Decision Management project . . . . . . . . . . . . . . . . . . . . . . . . 6.1.3 Combine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Deploy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.5 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 54 54 54 70 72 72

Chapter 7. Configuration of the risk assessment application for real-time scoring. . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Create a scoring configuration for the Decision Manager stream . . . . . . . . . . . . . 7.1.3 Use SPSS Collaboration and Deployment Services portal to test the scoring configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.4 Call the scoring service from a CICS transaction . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.5 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

73 74 74 74

Appendix A. Additional material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Locating the Web material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using the Web material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System requirements for downloading the Web material . . . . . . . . . . . . . . . . . . . . . . . . Downloading and extracting the Web material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

85 85 85 85 86

Related publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Help from IBM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 87 87 87 88

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

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Notices This information was developed for products and services offered in the U.S.A. IBM may not offer the products, services, or features discussed in this document in other countries. Consult your local IBM representative for information on the products and services currently available in your area. Any reference to an IBM product, program, or service is not intended to state or imply that only that IBM product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-IBM product, program, or service. IBM may have patents or pending patent applications covering subject matter described in this document. The furnishing of this document does not grant you any license to these patents. You can send license inquiries, in writing, to: IBM Director of Licensing, IBM Corporation, North Castle Drive, Armonk, NY 10504-1785 U.S.A. The following paragraph does not apply to the United Kingdom or any other country where such provisions are inconsistent with local law: INTERNATIONAL BUSINESS MACHINES CORPORATION PROVIDES THIS PUBLICATION "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Some states do not allow disclaimer of express or implied warranties in certain transactions, therefore, this statement may not apply to you. This information could include technical inaccuracies or typographical errors. Changes are periodically made to the information herein; these changes will be incorporated in new editions of the publication. IBM may make improvements and/or changes in the product(s) and/or the program(s) described in this publication at any time without notice. Any references in this information to non-IBM websites are provided for convenience only and do not in any manner serve as an endorsement of those websites. The materials at those websites are not part of the materials for this IBM product and use of those websites is at your own risk. IBM may use or distribute any of the information you supply in any way it believes appropriate without incurring any obligation to you. Any performance data contained herein was determined in a controlled environment. Therefore, the results obtained in other operating environments may vary significantly. Some measurements may have been made on development-level systems and there is no guarantee that these measurements will be the same on generally available systems. Furthermore, some measurements may have been estimated through extrapolation. Actual results may vary. Users of this document should verify the applicable data for their specific environment. Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. This information contains examples of data and reports used in daily business operations. To illustrate them as completely as possible, the examples include the names of individuals, companies, brands, and products. All of these names are fictitious and any similarity to the names and addresses used by an actual business enterprise is entirely coincidental. COPYRIGHT LICENSE: This information contains sample application programs in source language, which illustrate programming techniques on various operating platforms. You may copy, modify, and distribute these sample programs in any form without payment to IBM, for the purposes of developing, using, marketing or distributing application programs conforming to the application programming interface for the operating platform for which the sample programs are written. These examples have not been thoroughly tested under all conditions. IBM, therefore, cannot guarantee or imply reliability, serviceability, or function of these programs.

© Copyright IBM Corp. 2013. All rights reserved.

v

Trademarks IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. These and other IBM trademarked terms are marked on their first occurrence in this information with the appropriate symbol (® or ™), indicating US registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the Web at http://www.ibm.com/legal/copytrade.shtml The following terms are trademarks of the International Business Machines Corporation in the United States, other countries, or both: CICS® Cognos® DB2® IBM®

InfoSphere® Redbooks® Redbooks (logo) SPSS®

®

System z® WebSphere® z/OS®

The following terms are trademarks of other companies: Linux is a trademark of Linus Torvalds in the United States, other countries, or both. Windows, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both. Other company, product, or service names may be trademarks or service marks of others.

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Preface When ricocheting a solution that involves analytics, the mainframe might not be the first platform that comes to mind. However, the IBM® System z® group has developed some innovative solutions that include the well-respected mainframe benefits. This book describes a workshop that demonstrates the use of real-time advanced analytics for enhancing core banking decisions using a loan origination example. The workshop is a live hands-on experience of the entire process from analytics modeling to deployment of real-time scoring services for use on IBM z/OS®. In this IBM Redbooks® publication, we include a facilitator guide chapter as well as a participant guide chapter. The facilitator guide includes information about the preparation, such as the needed material, resources, and steps to set up and run this workshop. The participant guide shows step-by-step the tasks for a successful learning experience. The goal of the first hands-on exercise is to learn how to use IBM SPSS® Modeler for Analytics modeling. This provides the basis for the next exercise “Configuring risk assessment in SPSS Decision Management”. In the third exercise, the participant experiences how real-time scoring can be implemented on a System z. This publication is written for consultants, IT architects, and IT administrators who want to become familiar with SPSS and analytics solutions on the System z.

Authors This book was produced by a team of specialists from around the world working at the IBM International Technical Support Organization, Poughkeepsie Center. Mike Ebbers is an ITSO project leader in Poughkeepsie, NY. Keith Doan is an IBM employee in Australia. Andrew Flatt was an IBM employee in the United Kingdom when this book was written. Thanks to the following people for their contributions to this project: Richard Conway IBM International Technical Support Organization, Poughkeepsie Center

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Comments welcome Your comments are important to us! We want our books to be as helpful as possible. Send us your comments about this book or other IBM Redbooks publications in one of the following ways: 򐂰 Use the online Contact us review Redbooks form found at: ibm.com/redbooks 򐂰 Send your comments in an email to: [email protected] 򐂰 Mail your comments to: IBM Corporation, International Technical Support Organization Dept. HYTD Mail Station P099 2455 South Road Poughkeepsie, NY 12601-5400

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

1

Chapter 1.

Proof of Technology overview The Proof of Technology (PoT) overview described in this IBM Redbooks publication demonstrates the use of real-time advanced analytics for enhancing core banking decisions, in this case loan origination. The PoT is a live hands-on experience of the entire process from analytics modeling to deployment of real-time scoring services for use on IBM z/OS. Each participant will use a Windows 7 client for all development and deployment tasks.

© Copyright IBM Corp. 2013. All rights reserved.

1

1.1 Planning the Proof of Technology Note: Read the information in this document carefully before planning a session with your prospect. You need the following to conduct this PoT successfully: 򐂰 A facility with a classroom setup: – Overhead projector – Seating for a maximum of 20 participants (teams of two participants each) – A number of laptops or workstations with the appropriate client software •

Either stand-alone or connected to an ESX server installed locally

•

Ethernet or wireless network connectivity to the Internet

•

At least 8 GB RAM, and ideally more

•

Free hard disk space of at least 60 GB

– Internet connectivity 򐂰 Access to a System z environment with this host PoT software installed1 – You need to reserve time and space on a System z host – You also need to request certificates and user IDs to be able to access the System z environment over the Internet from each participant workstation 򐂰 Staff to conduct the PoT This PoT is not like most other PoTs. It might require multiple subject matter experts (SMEs) to run. For the following areas, you need to arrange for an SME to be available to present and to assist in the hands-on sessions: – Module 1: Analytics modeling with IBM SPSS Modeler •

IBM SPSS Modeler

– Module 2: Configure the Risk Assessment in SPSS Decision Management •

IBM SPSS Decision Management

– Module 3: Configuration of the Risk Assessment for Real-Time Scoring •

IBM SPSS Collaboration and Deployment Services

•

IBM CICS® TS on z/OS

•

IBM DB2® on z/OS

򐂰 Besides the above list of products, the facilitator team must be well versed in: – Service-oriented architecture (SOA), business process management (BPM), and Web Services concepts and technology – Data mining concepts – Using System z, z/OS, and Linux on System z, primarily to be able to start, stop, and restart servers, and do some troubleshooting

1

2

Contact the Poughkeepsie ITSO using [email protected] for suggestions.

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

1.2 Tasks to plan and execute the PoT This PoT requires a number of tasks to complete before delivery can take place.

1.2.1 Several weeks before the PoT delivery 1. Read this facilitator guide thoroughly. 2. Check with the client on possible delivery dates. 3. Discuss with your client the agenda of the PoT and discuss which modules that the client wants to perform. Modules have to be executed in a sequential order so it would require some work to run a subsection of them. 4. Reserve classroom equipment at the local TEC center, or arrange for classroom equipment at the client site. Ideally, you run the PoT with teams of two persons each. Plan for one or two spare machines. 5. Reserve the PoT System z environment on your chosen host for the dates requested. 6. Request certificates or user IDs. You need a certificate or user ID for each classroom machine and a few spare ones. Therefore, if you plan to use 10 classroom machines, you should request at least 12 certificates or user IDs. 7. You need to install the PoT client software on each team’s workstation. 8. Plan the required IBM staffing.

1.2.2 About one week before the PoT delivery 1. Obtain the following assets: a. b. c. d.

Lab scripts Presentations Facilitator guide Workstation client software

2. Send the lab scripts and presentation slides to the reproduction department for duplication. Each participant requires a complete set of hardcopy material.

1.2.3 As soon as you have access to the System z system STOP! Before proceeding, you need to ensure that the host names used in the labs point to the correct IP addresses. Perform these steps as soon as possible, but no later than a few days before PoT delivery: 1. Ensure that the host names point to the correct IP addresses in the etc/hosts file in the client. Comment and uncomment entries as required and save the changes. 2. Test the connectivity with the System z environment: Open a Telnet, browser, or Personal Communications session and use the IP addresses as explained in 2.1, “Preparing the System z environment” on page 10. Ensure that you check connectivity with the z/OS logical partition (LPAR), as well as both z/Linux systems. 3. Test availability of all required software on System z, using the information in 2.1, “Preparing the System z environment” on page 10.

Chapter 1. Proof of Technology overview

3

4. Eventually, clean the environment, again using the information in 2.1, “Preparing the System z environment” on page 10. 5. Run through the modules to ensure that you are familiar with the content and that the systems are working correctly. 6. Copy the client workstation software to the other machines.

1.2.4 Just before PoT delivery Before delivery of PoT, proceed with the following steps: 1. Verify the license for Windows 7. 2. Verify the license for the SPSS software.

1.3 Flow The PoT basically consists of presentations and practical modules. The time that is required for each presentation and module is variable. The length of the PoT is 4 - 6 hours, depending on how much you present and how smoothly the attendees go through the modules: 򐂰 򐂰 򐂰 򐂰 򐂰 򐂰 򐂰

Presentation 1: General SPSS Solution Overview Presentation 2: Business Scenario Presentation 3: Module Outline Module 1: Analytics modeling with IBM SPSS Modeler Module 2: Configure the Risk Assessment in SPSS Decision Management Presentation 4: Putting it all together Module 3: Configuration of the Risk Assessment for Real-Time Scoring

Figure 1-1 on page 5 shows a logical representation of the artifacts that are required for scoring data.

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

1.3.1 Design Logical representation of the artifacts required for scoring data SPSS Modeler Training import

SPSS Decision Manager

Stream

Decision Manager Stream Deploy stream

Scoring import

+

Rules

Deploy Import Import

SPSS Collaboration and Deployment Services Stream

Analytic DPD

Operational DPD

Real-time DPD

Rules

Decision Manager Stream

Real-time data Scoring Configuration

Application View Enterprise View

DB2

DB2

Figure 1-1 Logical representation of the artifacts required for scoring data

Figure 1-2 on page 6 shows a logical representation of the actual artifacts that are required for scoring data in these modules.

Chapter 1. Proof of Technology overview

5

1.3.2 Actual implementation Logical representation of the actual artifacts required for scoring data SPSS Modeler Training import

SPSS Decision Manager

Individual_Risk_Assessment

Individual_Risk_Assessment_DM_V3

Deploy stream

Scoring import

+ Import

Rules

Import Deploy

SPSS Collaboration and Deployment Services Individual_Risk_Assessment.str

Allocation_Rules.rul

Individual_Risk_Assessment_DM_V3.str

Risk_DPD_OP

Risk_DPD_Analytic

RiskRTDPD

Real-time data RISKSCOREV1

Risk_App_View Enterprise View

DB9A

Figure 1-2 Logical representation of the actual artifacts required for scoring data in these modules

Figure 1-3 on page 7 shows a logical representation of the architecture that is required for real-time scoring from CICS.

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Logical representation of the architecture required for real-time scoring from CICS CICS Region Web Service Requester Application

SPSS Collaboration and Deployment Services

Analytic DPD

Operational DPD

Real-time DPD

Decision Manager Stream

Web Services

Scoring Configuration

Scoring Engine

Real-time data

Application View Enterprise View

DB2

DB2

Figure 1-3 Logical representation of the architecture required for real-time scoring from CICS

1.4 Modules The PoT is set up in a modular way. Each module has an “initial” state, which is the situation that the participant starts with, and a “solution” state, which is the situation that the participant should end with. Each module requires the initial state to be the solution state of the previous module. The following modules are available. Table 1-1 PoT modules Module

Activities/topics

Module 1: Analytics modeling with IBM SPSS Modeler

򐂰 򐂰 򐂰 򐂰

Import data into SPSS Modeler Create an SPSS Modeler analytics model Test an SPSS Modeler analytics model Deploy the SPSS Modeler analytics model

Module 2: Configure the Risk Assessment in SPSS Decision Management

򐂰 򐂰 򐂰 򐂰 򐂰 򐂰

Configure a Risk Assessment application Connect Data Define Outcomes Configure Operational Decisions with Rules and Models Combine Rules and Analytical models to optimize decision outcomes Deploy a Risk Assessment application

Module 3: Configuration of the Risk Assessment for Real-Time Scoring

򐂰

Configure an SPSS Decision Management stream for real-time scoring Test the real-time scoring configuration Invoke the real-time scoring from a CICS TS Transaction

򐂰 򐂰

Chapter 1. Proof of Technology overview

7

1.5 Handouts Handouts need to be reproduced in hardcopy. Note: Each participant needs to receive a hardcopy of all lab scripts scheduled. In most cases, it is acceptable to provide the presentations in softcopy format.

1.5.1 Presentations Presentations are provided in PowerPoint format. Therefore, you can do your own customizing for your session. The presentation materials should be regarded as a “superset” so that you can reduce the content to your technical comfort level. You can also replace the provided slide decks with your own slide decks, if they fit well.

1.6 Products used Figure 1-4 illustrates which products are used and how they integrate with each other.

Physical architecture z/OS LPAR wtsc90.itso.ibm.com

CICS Region

z/Linux platform.itso.ibm.com

Windows VM

SPSS Modeler Client SPSS Modeler Server SPSS Deployment Manager z/Linux analytics.itso.ibm.com DB2 Tools

DB2

SPSS Collaboration and Deployment Service deployed on WAS

Figure 1-4 PoT hardware and software environment

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

2

Chapter 2.

PoT technical overview The System z environment for this Proof of Technology (PoT) can be hosted on a standard System z platform that is running the required software. See 1.2, “Tasks to plan and execute the PoT” on page 3.

© Copyright IBM Corp. 2013. All rights reserved.

9

2.1 Preparing the System z environment The System z environment consists of a z/OS logical partition (LPAR) and two Linux systems. The systems listed below are examples only, for your use in setting up your host system.

2.1.1 z/OS LPAR Note: You need to map the host name used to the IP address that you are going to use in the etc/hosts file in the clients. If you are providing your own host, you need to add a line in etc/hosts to make the mapping. You need to comment/uncomment the entries depending on where you run the PoT. The following software products are used on z/OS: 򐂰 CICS Transaction Server V4.1 򐂰 DB2 for z/OS V9.1 Note: The above software might or might not be running and you need to verify for each one whether it is running or not. Instructions follow below to start or restart parts of the environment.

STOP: If you have no experience with z/OS, contact somebody you know with z/OS experience.

Sample connection details Host name ITSO

wtsc90.itso.ibm.com

IP address POK Yellow 10.52.52.121 User ID

ITSO99

Password

ITSO99

CICS Transaction Server Each team uses the same CICS region, named CITSO99. Below are the characteristics. Substitute “XX” with the team number.

10

CICS host name

wtsc90.itso.ibm.com

CICS port

3099

CICS started task

SYS1.PROCLIB(CITSO99)

CICS start command

S CITSO99

CICS stop command

C CITSO99

CICS terminal

Point your Personal Communications (PCOMM) at wtsc90.itso.ibm.com, port 23, and enter CITSO99

CICS user ID

ITSOXX

CICS Password

ITSOXX (never expires)

CICS IBLA datasets

ITSO99.IBLA.*

CICS libs

CICSTS41

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

IBLA application The ITSO Bank Loan Application (IBLA) is a custom-designed and simplified back-end application that provides certain functions in initiating and maintaining a loan.

Initialization of the CICS environment Note: Prior to running the PoT, it is strongly recommended to reset the CICS environment. Follow the below procedure to do this. 򐂰 In data set ITSO99.IBLA.JCL, run jobs ITSO01L through ITSO12L. 򐂰 After starting/restarting each CICS region, you have to install the groups IBLA and SPSS: CEDA I GRP(IBLA) CEDA I GRP(SPSS)

Some troubleshooting techniques: 򐂰 SDSF LOG 򐂰 SDSF sysout of CICS region 򐂰 In CICS, CEMT I PROG(<progname>), to check transaction count

DB2 The operational data used by the IBLA application is stored in DB2 tables. All participants use the same set of tables. Below is the technical relevant information for the DB2 environment: DB2 host name

wtsc90.itso.ibm.com

DB2 TCP port

38350

DB2 RES port

38351

DB2 location name

DB9A

DB2 SSN

DB9A

DB2 JDBC HFS

/usr/lpp/db2/db9a/db2910_jdbc

DB9A setup JCL

DB9AU.SDSNSAMP

DB9A ISPF panels

Enter 9A from main ISPF panel

DB9A volumes

STORCLAS=DB9A

Check if data exists in DB2 tables Create the analytics table that is to be used for SPSS modeling and scoring by following these steps: 򐂰 Open SPSS Modeler and retrieve the Modeler stream from the Repository at, \Individual Risk Assessment\Data\CheckData.str. 򐂰 Within CheckData.str, run the database source nodes to check if data exists. If not, run the flow to populate required data.

2.1.2 z/Linux system: platform Important: Use Putty to access the z/Linux systems (SSH on Port 22).

Chapter 2. PoT technical overview

11

Note: You need to map the host name used to the IP address that you are going to use in the etc/hosts file in the clients. If you are providing your own host, you need to add a line in etc/hosts to make the mapping. You need to comment/uncomment the entries depending on where you run the PoT. This system hosts SPSS Collaboration and Deployment Services running on IBM WebSphere® Application Server (WAS). The artifacts for this Linux system are: Host name ITSO

platform.itso.ibm.com

IP address POK Yellow 10.52.78.13 User ID

root

Password

rootpw

DB2 A DB2 database is also used to support SPSS Collaboration and Deployment Services. The artifacts are: 򐂰 Starting the DB2 server: – – – –

su db2inst1 (password db2inst1, if prompted) db2stop db2start Exit to change back to root

򐂰 Product path: /code/IBM/db2/V9.5 򐂰 Instance path: /code/IBM/db2inst1

SPSS Collaboration and Deployment Services running on WebSphere Application Server The artifacts for WAS are: WAS admin console https://:9043/ibm/console/logon.jsp Path to profile

/code/IBM/WebSphere/AppServer/profiles/AppSrv01

CnDS Username

admin

CnDS Password

passw0rd

Restarting the WAS server A restart of SPSS Collaboration and Deployment Services might be necessary, which is achieved by restarting the WAS server hosting SPSS Collaboration and Deployment Services: 򐂰 cd /code/IBM/WebSphere/AppServer/profiles/AppSrv01/bin 򐂰 ./stopServer.sh server1 򐂰 ./startServer.sh server1

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Initialization of the SPSS Collaboration and Deployment Services repository After each PoT, the SPSS Collaboration and Deployment Services repository may need to be cleaned. This can be done manually very easily, as follows: 򐂰 Use SPSS Collaboration and Deployment Services Deployment Manager. 򐂰 Log on to SPSS Collaboration and Deployment Services using the CnDS username and password. 򐂰 Expand the content repository. 򐂰 Delete all the files under each ITSOXX folder, as shown in Figure 2-1.

Figure 2-1 Content Repositories starting point

򐂰 Go to View  Show View  Scoring. 򐂰 Select any RISKSCORINGXX configurations and right-click them. 򐂰 Click Delete Configuration(s).

2.1.3 z/Linux system: analytics Important: Use Putty to access the Linux systems (SSH on Port 22). This system hosts SPSS Modeler Server. The artifacts for this Linux system are: Host name ITSO

analytics.itso.ibm.com

IP address POK Yellow 10.52.78.12 User ID

root

Password

rootpw

Chapter 2. PoT technical overview

13

SPSS Modeler Server Perform the following actions to stop, start, or check if the SPSS Modeler Server is started.

Stopping the SPSS Modeler Server 򐂰 To stop the SPSS Modeler server run: /code/IBM/SPSS/ModelerServer/14.2/ # ./modelersrv.sh stop

Starting the SPSS Modeler Server: 򐂰 To start the SPSS Modeler server run: /code/IBM/SPSS/ModelerServer/14.2/ # ./modelersrv.sh start

Checking if the SPSS Modeler Server is started 򐂰 To check if the SPSS Modeler server has started to run: /code/IBM/SPSS/ModelerServer/14.2/ # ./modelersrv.sh list

2.2 Preparing the clients This PoT uses software on a Windows workstation for all the client applications required.

2.2.1 Client software 򐂰 SPSS Modeler Client 򐂰 SPSS Collaboration and Deployment Services Deployment Manager 򐂰 DB2 Tools

2.3 Final checks Here are some tasks for checking your setup.

2.3.1 Run the scoring transaction to test setup To test that the scoring transaction can run, the first thing that you need to do is open a 3270 terminal to the mainframe. You do this by logging on to the CICS region so that you can invoke a transaction to call the scoring service. Use IBM Personal Communications. __ 1. Double-click the wtsc90 session. __ 2. Set up a connection to the z/OS machine. __ 3. You should see a logon panel similar to Figure 2-2 on page 15. __ 4. To log in to the CICS region, type “CITS099”. __ 5. Press the right control button (Ctrl) to enter the command. __ 6. Enter the user ID “ITSO99” and the password “ITSO99”. __ 7. Press the right control button (Ctrl) to enter the logon credentials. __ 8. You should see a message like DFHCE3549 Sign-on is complete, which informs you that your logon was successful.

14

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Figure 2-2 ITSO logon panel

Figure 2-3 shows the panel to sign on to CICS.

Figure 2-3 Sign on to CICS panel

__ 9. Press the pause button to clear the display. __ 10. In the top right corner, type the transaction name “SCR1”, as shown in Figure 2-4 on page 16.

Chapter 2. PoT technical overview

15

Figure 2-4 Entering the scoring transaction

__ 11. Press the right control button (Ctrl) to submit the transaction to CICS. __ 12. You should now see the scoring application CICS user interface (UI). This will look similar to Figure 2-5.

Figure 2-5 CICS Scoring 3270 user interface

__ 13. Enter a Loan ID. This can be a number 1 - 9. __ 14. Enter an Amount. __ 15. Press the right control button (Ctrl) to submit the inputs for scoring.

16

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

__ 16. You now can see that a scoring algorithm ran and returned a risk score and a recommend action. See Figure 2-6 for a successful scoring execution. Note: The formatting causes the inputs to have leading zeros, that is, 000000003.

Figure 2-6 Successful scoring execution

Note: If you see an error message displayed in the application, check that the scoring configuration still exists and has access to the host files.

Chapter 2. PoT technical overview

17

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

3

Chapter 3.

Preparations before you start This chapter gives you information about how to use this guide and provides you with the information that you need to perform the tasks in the worksheet.

© Copyright IBM Corp. 2013. All rights reserved.

19

3.1 Important reading guidelines It is important to read this section before you start any lab activities. Attention: Do not start with any lab before you have read the instructions below. 򐂰 Review and complete the worksheet where necessary. Detach the sheet from the lab exercise book and place it on your table. You need to refer to the worksheet continuously. 򐂰 User IDs, host names, and other location-dependent variables are referred to as variable names in the lab scripts using the following font: variable name. Refer to your worksheet for the value of the variable to be used in your lab. 򐂰 When you see a check box symbol “__1”, it means that you have to “do” something on your computer—not merely read the document. 򐂰 If you see user IDs, passwords, file names, and so on, with an “xx” or “XX” in it, it usually means that you need to replace those characters with the number of the user ID that has been assigned to you. If in doubt, ask the facilitator. 򐂰 If you see a discrepancy between the screen capture and the text explaining the step, use the information in the text. Some screen captures do not exactly reflect the names and values that are used in the labs. 򐂰 Most errors occur because of typing errors. Ensure that you type exactly the values as printed in the document. Most of the values are case-sensitive.

20

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

3.2 Worksheet In the labs many variables are used, such as user IDs, passwords, and port numbers. Instructions: 򐂰 Review and complete this worksheet where necessary before starting with the labs. 򐂰 Copy or detach this worksheet from your book and place it on your table for reference during the labs. You will need to refer to this worksheet frequently.

Your team number: ______ Use this team number as a substitute for ‘XX’ when indicated.

Chapter 3. Preparations before you start

21

3.3 General system information

22

Variable

Your value

Modeler Stream Name

Individual_Risk_Assessment.str

Decision Management URL

http://platform.itso.ibm.com:9080/DM/

DM User

admin

DM Password

passw0rd

CnDS User

admin

CnDS Password

passw0rd

Platform Hostname

platform.itso.ibm.com

Platform Port

9080

Group Folder

ITSOnn, where ‘nn’ is your team number

Decision Management Project Name

Individual_Risk_Assessment_DM.str

Scoring ID

RISKSCORINGnn, where ‘nn’ is your team number (MUST BE ENTERED IN CAPITALS)

z/OS Hostname

wtsc90.itso.ibm.com

z/OS User ID

ITSOnn, where ‘nn’ is your team number

z/OS Password

ITSOnn, where ‘nn’ is your team number

CICS Region Name

CITSO99

Screen Size

32x80

Scoring Transaction

SCR1

Scoring Address

PLATFORM.ITSO.IBM.COM

Scoring Port

9080

z/Linux Username

root

z/Linux Password

rootpw

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

4

Chapter 4.

Worksheet In the labs many variables are used, such as user IDs, passwords, and port numbers. Instructions: 򐂰 Review and complete this worksheet where necessary before starting with the labs. 򐂰 Detach this worksheet from your book and place it on your table for reference during the labs. You will need to refer to this worksheet frequently.

Your team number: ______ Use this team number as a substitute for ‘XX’ when indicated.

© Copyright IBM Corp. 2013. All rights reserved.

23

4.1 General system information

24

Variable

Your value

Modeler Stream Name

Individual_Risk_Assessment.str

Decision Management URL

http://platform.itso.ibm.com:9080/DM/

DM User

admin

DM Password

passw0rd

CnDS User

admin

CnDS Password

passw0rd

Platform Hostname

platform.itso.ibm.com

Platform Port

9080

Group Folder

ITSOnn, where ‘nn’ is your team number

Decision Management Project Name

Individual_Risk_Assessment_DM.str

Scoring ID

RISKSCORINGnn, where ‘nn’ is your team number (MUST BE ENTERED IN CAPITALS)

z/OS Hostname

wtsc90.itso.ibm.com

z/OS User ID

ITSOnn, where ‘nn’ is your team number

z/OS Password

ITSOnn, where ‘nn’ is your team number

CICS Region Name

CITSO99

Screen Size

32x80

Scoring Transaction

SCR1

Scoring Address

PLATFORM.ITSO.IBM.COM

Scoring Port

9080

z/Linux Username

root

z/Linux Password

rootpw

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

5

Chapter 5.

Analytics modeling with IBM SPSS Modeler This lab takes you through the steps of creating and deploying predictive models for analytical insight in our chosen loan risk assessment scenario using the IBM SPSS Modeler.

© Copyright IBM Corp. 2013. All rights reserved.

25

5.1 Background reading: Modeling and IBM SPSS Modeler Data mining is a general term that refers to a variety of modeling techniques that identify nuggets of information in (large) bodies of data, without necessarily having preconceived notions about what will be discovered. Data mining extracts information in such a way that it can be used in areas such as decision support, prediction, forecasts, and estimation. Data is often voluminous but of low value and with little direct usefulness in its raw form. It is the hidden information in the data that has value. Data mining is an interactive and iterative process. Success comes from combining your (or your expert's) knowledge of the data with advanced, active analysis techniques in which the computer identifies the underlying relationships and features in the data. The process of data mining generates models from historical data that are later used for predictions, pattern detection, and more. The technique for building these models is called machine learning, or modeling. In this lab, we have a sample of historical loan data with known outcomes to create a machine learning model to predict how likely a new individual loan is approved based on its data-driven risk profile. To evaluate the model performance, we partition the existing data for “training” a model and then “testing” it against the performance metrics such as overall prediction accuracy.

5.1.1 Key questions for a modeling project These are key questions before starting a modeling project: 1. Is data available? Data needs to be stored in an easily accessible format. Often the data is stored in different locations or formats that need to be pulled together before analysis. There are also potential limitations, such as legal or political reasons, why data cannot be accessed. 2. Does the data cover relevant factors? It is important that the data contains all the relevant factors and variables. Often, an objective of data mining is to help identify the relevant factors in the data. If thought is given to this question, you achieve a greater prediction accuracy. 3. Is the data erroneous? The more erroneous or missing data, the more difficult it will be to make accurate predictions. IBM SPSS Modeler capabilities have been shown to successfully handle data consisting of data made up by 50% errors. 4. Is there enough data? The answer depends on the individual problem. It is not often the amount of data that causes difficulties in modeling. It is the attempt to represent the target population and cover all possible outcomes. 5. Is the expertise on the data available? Successful modeling projects require domain expertise that is practical and relevant. We require knowledge about how the data was generated, the data characteristics, how the data is used, and what the intended use is of a modeling project. The domain expert guides the project in identifying relevant factors, helps interpreting the results, and sorts out the truly useful pieces of information from a business perspective. Because the objective of this module is to illustrate the proof of technology, we have simplified the data for facilitating modeling and deployment activities for expediency in a training course.

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

5.1.2 Introduction to IBM SPSS Modeler IBM SPSS Modeler is a data mining workbench that supports all the steps in the data mining process. IBM SPSS Modeler can run in local mode or distributed (client-server) mode. In this lab, you run IBM SPSS Modeler in distributed mode, as shown in Figure 5-1.

DB2 z/OS wtsc90.itso.ibm.com

zLinux Server: analytics.itso.ibm.com

SQL tables Data Modeler Server

Windows 7 Client

Modeler Client

Figure 5-1 Modeler server topology

5.1.3 IBM SPSS Modeler benefits IBM SPSS Modeler is easy to learn because it has an intuitive visual interface and requires no programming. The SPSS Modeler workbench offers a comprehensive range of data mining functions with powerful automation including automated data preparation and multi-model creation and evaluation. The SPSS Modeler is based on an open and scalable architecture that allows for: 򐂰 SQL pushback support 򐂰 Maximized use of infrastructure with multithreading, clustering, and use of embedded algorithms (in-database mining) 򐂰 Integration with IBM technologies such as IBM Cognos® and IBM InfoSphere® Warehouse

SPSS Enterprise View Enterprise View (EV) is a lightweight virtual data layer that can bring together multiple data sources in one virtual layer. This streamlines data access for the business and can be integrated with existing data management technologies of the enterprise—including enterprise data warehouses, entity management, and master data management.

Chapter 5. Analytics modeling with IBM SPSS Modeler

27

EV consists of tables that list all data elements that can be used throughout the organization. EV provides the necessary separation of concerns for analytics users. They do not have to consider physical data sources at modeling time. It decouples them from the impact of physical data sources changing. When building individual analytical applications, only a subset of all the elements are required. These required elements are defined in the Application View by mapping to selected EV tables. Adding a new data source such as a loan payment history, involves mapping the new source by defining a Data Provider Definition (DPD). When that has been defined, the new attributes can be leveraged within the analytics and operational environment for rules, arbitration inputs, or model inputs. The DPD is the only component that actually knows where to query and collect the required data to pass to the Application View. In this Proof of Technology (PoT), DPDs differ per environment: Training for analytics, scoring for operational, and real-time scoring where data is collected from a combination of inside the request message (which is defined as context data) and the database. Functionally, Training represents the historical data with known outcomes. Scoring represents the loan transactions that require prediction through scoring, hence, without the predicted field. Real-time scoring represents the real-time data that requires the real-time input data by users for individual transaction scoring.

Figure 5-2 SPSS Enterprise View concepts

28

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

5.2 What this lab is about In this lab, you retrieve loan data, prepare the data, build models, evaluate them, and then deploy them into the operational environment. Important: Each time that you see this check box “__ 1”, it means that you have to do something on your workstation in addition to reading the document.

5.2.1 What will you learn? At the end of this lab exercise, you should have learned: 򐂰 򐂰 򐂰 򐂰 򐂰 򐂰

How to create predictive models in IBM SPSS Modeler How to connect and retrieve enterprise data for the loan risk assessment scenario How to prepare data for modeling How to create models using the automated modeling feature How to evaluate models that meet your modeling objective How to deploy your chosen model to your operational environment for business application

5.2.2 Prerequisites 򐂰 You should have access to the client workstation where you will be performing the lab exercise. Check with your lab facilitator to get access. 򐂰 You should have received your team number from the lab facilitator. You will be using this number during lab and it will determine variables in the worksheet.

5.3 Building a predictive model You open the SPSS Modeler workbench to create a workflow for building a model. You connect data to the training data view, a specific enterprise view for building model. You then partition the input data and set each data field to a role. When the data is ready, you create a model for the sample data using automated modeling. The resulted model will be evaluated to ensure that the most appropriate model is chosen for deployment. Important: All activities are to be performed on the client.

5.3.1 Starting SPSS Modeler Workspace __ 1. Start IBM SPSS Modeler: Start  All Programs  IBM SPSS Modeler 14.2  IBM SPSS Modeler 14.2. __ 2. You are presented with the Server Login dialog. See Figure 5-3 on page 30.

Chapter 5. Analytics modeling with IBM SPSS Modeler

29

Figure 5-3 IBM SPSS Modeler server login

__ 3. Set the User ID to the z/Linux Username from the worksheet __ 4. Set the Password to the z/Linux Password from the worksheet __ 5. Click OK. __ 6. You are then presented with the Modeler Workbench. See Figure 5-4 on page 31.

30

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Figure 5-4 BM SPSS Modeler Workbench

5.3.2 Connecting to data In this next step, you set up the connection and partition the data. This data includes history on payments, income, assets, age, occupation, and other demographic data to enable the bank to assess credit worthiness of the loan applicant. In the scope of this lab, the use of the EV data capability means that users do not have to be concerned with physical data sources (see “SPSS Enterprise View” on page 27 for more explanation). You want to partition the data into two sets: Training and Testing. You want to only use 50% of the data for training so that you can see how the resulting models perform against the test data. If you were to train and test against the same data, you may end up with a model that is too specific to the training data and not generic enough to be applied in the application. Then, you provide types and roles to the data so that the model can use the inputs effectively to derive a target value. Follow these steps: __ 7. Double-click the Enterprise View node (under the Sources tab). __ 8. Double-click the Partition node and Type node (under the Field Ops tab). __ 9. Validate that you have a workflow that connects the Enterprise View node to the Partition node, then from the Partition node to the Type node. This configuration is shown in Figure 5-5 on page 32.

Chapter 5. Analytics modeling with IBM SPSS Modeler

31

Figure 5-5 SPSS Modeler Workflow

Steps 10 - 18 on page 33 are to establish the connection to EV data that is managed by the repository: __ 10. Double-click the Enterprise View to show the dialog to specify the application view and table from which to read data. __ 11. Click the Connection drop-down menu. __ 12. Select Add/Edit a connection, as shown in Figure 5-6.

Figure 5-6 Enterprise View dialog

A Repository Server connection dialog opens, as shown in Figure 5-7.

Figure 5-7 Repository Server connection dialog

32

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

__ 13. Enter the Platform Hostname from your worksheet in to the Repository input. __ 14. Enter the Platform Port from your worksheet into the Port input. __ 15. Click OK. A Repository Credentials dialog opens, as shown in Figure 5-8.

Figure 5-8 Repository Credentials dialog

__ 16. Enter the CDS User from your worksheet into the User ID input. __ 17. Enter the CDS Password from your worksheet into the Password input. __ 18. Click OK. When the repository connection is established, an Enterprise View Connections dialog opens, as shown in Figure 5-9.

Figure 5-9 Enterprise View Connections dialog

Steps 19 - 28 on page 34 are to select the required training data for creating predictive models. The training data is represented by the EV: Risk_App_View\Risk_DPD_Analytics\Risk_Training_Data. __ 19. Click the top-right icon (it has a red arrow in it). __ 20. Open the Individual Risk Assessment folder. __ 21. Click Risk_App_View to select it. Chapter 5. Analytics modeling with IBM SPSS Modeler

33

__ 22. Click OK. __ 23. Set the data provider to Risk_DPD_Analytic. __ 24. Check the configuration, as shown in Figure 5-10. __ 25. Click OK.

Figure 5-10 Enterprise View Connections dialog

__ 26. Press Select next to the Tables input. __ 27. Click Risk_Training_Data, as shown in Figure 5-11 on page 35. __ 28. Click OK.

34

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Figure 5-11 Enterprise View table dialog

Important: If you encounter a problem while connecting to the repository server, ask your lab facilitator to verify your firewall authentication. __ 29. Now the Connection and Table are selected. Click Preview to view the first rows of input data. __ 30. After examining, click OK. See Figure 5-12. __ 31. Click OK in the Enterprise View dialog.

Figure 5-12 Preview the first rows of input data

Chapter 5. Analytics modeling with IBM SPSS Modeler

35

__ 32. Double-click the Partition node to open the dialog for setting the data into two partitions: Training and Testing, as shown in Figure 5-13.

Figure 5-13 Partition node dialog

__ 33. Click OK to accept the default values. Now you have the input data to be partitioned into 50% training and 50% testing. Steps 34 - 37 on page 37 enable the analyst to determine how each input data field will be used for creating predictive models. __ 34. Double-click the Type node to open the dialog, as shown in Figure 5-14 on page 37.

36

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Figure 5-14 Type node dialog

__ 35. Locate the following data fields and set appropriate Measurement values from the available list, as shown in Figure 5-15 on page 38: • • • • •

education as Typeless LOANID as Typeless residential as Typeless STATUS as Typeless is_approvable as Flag

__ 36. For is_approvable, set the Role as Target. __ 37. Click OK. Note: is_approvable is the flag data field that we want to predict with the value 1 or 0 (True or False). Because other fields in the preceding list do not add value to the modeling, we ignore them by setting their roles as Typeless, as shown in Figure 5-15 on page 38.

Chapter 5. Analytics modeling with IBM SPSS Modeler

37

Figure 5-15 Enter Type node dialog

What you have done so far: 򐂰 In this section, you connected to an Enterprise View data source to retrieve an appropriate data view for modeling. 򐂰 Partitioned the input data for training and testing. 򐂰 Set the modeling role to each input data field.

5.4 Building a model You should now be in a position to create a predictive model from the input data. In this section, we use the SPSS automated modeling capability so that you can use the modeler to find the best algorithm for the data. Follow these steps: __ 1. Select the Auto Classifier node (under Modeling tab) to connect from the Type node, as shown in Figure 5-16 on page 39. The Auto Classifier is an automated modeling node that generates prediction by combining the best ranked algorithms over the set of available algorithms.

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Figure 5-16 Modeler Workflow with Auto Classifier Modeling node

Note: The name of the Auto Classifier node becomes the name of the target field, in this case, is_approvable. __ 2. Double-click the is_approvable node to open the dialog. See Figure 5-17 on page 40.

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Figure 5-17 Model tab dialog of Auto Classifier

In the dialog above, the automated modeling node executes nine models. The criteria used to compare and rank models include overall accuracy, area under the receiver operating characteristic (ROC) curve, profit, lift, and number of fields. The three best ranked models are combined for the final model. Note: In the Model tab that is shown in Figure 5-17, the Auto Classifier node uses partitioned data and retains the three best ranked models. __ 3. Select the Expert tab. You see there are nine available algorithms to select from; hence, the Auto Classifier retains the best three out of nine models. See Figure 5-18 on page 41.

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Figure 5-18 Expert tab dialog of Auto Classifier node

__ 4. Select Run to execute the node. When the execution completes, the generated model appears as shown in Figure 5-19.

Figure 5-19 Generated Auto Classifier Model

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__ 5. Double-click the generated is_approvable nugget node to view the modeling results. See Figure 5-20.

Figure 5-20 Auto Classifier modeling results

You see that the overall accuracy of the logistic regression is higher than the other two. However, it uses more data fields than the others: 11, versus 6 and 7. Note: It is preferable for models to use fewer fields because it provides less demand for data, more efficient execution, and a more generic model. In short, you can achieve very similar results and require fewer fields. Steps 6 - 8 are to decide how to combine the best three ranked algorithms for the final model: __ 6. Click the Settings tab. __ 7. Set the ensemble method to Average raw propensity. __ 8. Click OK, as shown in Figure 5-21. This method selection enables the approvable propensity score to be generated for later use in a loan risk assessment application.

Figure 5-21 Selecting Ensemble method for automated modeling

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Note: The resulted model is generated by the ensemble capability that combines the three best ranked models to obtain more accurate predictions than can be gained from any of the individual models. By combining predictions from multiple models, limitations in individual models may be avoided, resulting in a higher overall accuracy. Models that are combined in this manner typically perform at least as well as the best of the individual models, and often better. __ 9. Click the Output tab. __ 10. Double-click the Analysis node. This is used to evaluate the resulting model performance. See Figure 5-22.

Figure 5-22 Evaluate resulting model by the Analysis node

__ 11. Double-click the Analysis node to open the dialog, as shown in Figure 5-23 on page 44.

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Figure 5-23 Analysis dialog

__ 12. Click Run to execute the node with the default values and the result, as shown in Figure 5-24.

Figure 5-24 Model Analysis output

The Analysis output suggests that only 52 out of 1929 records, or 2.7% of the training data, are wrongly predicted by the model. Similarly, only 73 out of 2071, or 3.52% of the testing data, are wrongly predicted by the model.

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With such low percentages of the model errors for both training and testing data, the resulted model should meet the performance requirement for the scenario. __ 13. Click OK. What you did in this section: 򐂰 In this section, you successfully created a predictive model for the sample data using the automated modeling capability via Auto Classifier node. 򐂰 You evaluated the performance of the model against the training and testing data using the Analysis node.

5.5 Deploying a model In this section, we create a Scoring workflow that deploys the resulted model to your operational analytics environment for business applications.

5.5.1 Creating a scoring workflow You will connect to the Scoring data view, which has all the data fields as the Training data view except the target field (is_approvable). The previous generated model from the Training workflow will then be used to process the input scoring data and then output to a Table node. Steps 1 - 13 on page 33 are performed to select the required scoring data for executing resulted predictive models. The scoring data is represented by the EV: Risk_App_View\Risk_DPD_OP\Risk_Scoring_Data. __ 1. Click the Sources tab. __ 2. Double-click the Enterprise View data source to create a new one. This puts the Enterprise View somewhere on the canvas. If it is in the middle of the flow, you can click and drag it to somewhere convenient. __ 3. Double-click it to open the dialog. __ 4. Select to add a new Enterprise View connection by clicking the Connection drop-down box and selecting Add/Edit a Connection. Figure 5-25 on page 46 shows how to add a new connection.

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Figure 5-25 Add a new connection

__ 5. Click Risk_DPD_Analytic to select it. __ 6. Set the Application view to Risk_App_View. __ 7. Set the Environment to Operational. __ 8. Set the Data Provider to Risk_DPD_OP, as shown in Figure 5-26.

Figure 5-26 Add an Operational data view

__ 9. Click OK.

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__ 10. Click the Select button next to the Tables input. __ 11. Click Risk_Scoring_Data to select it. __ 12. Click OK in the Tables view. See Figure 5-27. __ 13. Click OK.

Figure 5-27 Select Table name

In steps 14 - 21 on page 48, you copy the resulted predictive model to include in the stream for scoring: __ 14. Right-click the is_approvable nugget node. __ 15. Click Copy Node. __ 16. Right-click the Canvas. __ 17. Click Paste to paste the nugget into the canvas. __ 18. Drag the pasted node next to the Enterprise node that you just created. __ 19. Right-click the Enterprise node that you just created. __ 20. Click Connect.

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__ 21. Now click the copied is_approvable node to connect, as shown in Figure 5-28.

Figure 5-28 Create scoring workflow

__ 22. Click the Output tab. __ 23. Click the copied is_approvable node to select it. __ 24. Double-click the Table node. This connects to the is_approvable node, as shown in Figure 5-29.

Figure 5-29 Add Table node to the scoring workflow

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In steps 25 - 28, you execute the data to produce the predictive results that are captured in the data field $XFRP-is_approvable with values being close to “one”. This value indicates that it is more likely to be approved, and being close to “zero”, indicates that it is more likely to be rejected. __ 25. Right-click the Table node to select Run. __ 26. As the Scoring Table results open, scroll to the right side of the table to see the results, as shown in Figure 5-30. __ 27. Click OK after you examine the table results.

Figure 5-30 Scoring output

$XF-is_approvable value is the prediction of whether a loan is approvable. $XFRP-is_approvable value is the calculated propensity for approving loans, which will be used for assessing an individual loan in the business application. __ 28. Click OK.

5.5.2 Deploying scoring workflow In this section, you deploy the Scoring workflow to your analytical platform. Follow these steps: __ 29. Click File. __ 30. Click Store. __ 31. Click Deploy, as shown in Figure 5-31 on page 50.

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Figure 5-31 Deploy modeler stream to repository

In steps 32 - 39 on page 52, you deploy the scoring stream into the repository for operational application in the enterprise environment. When it is done, the predictive model can be used in a batch operation for scoring batch input transactions or in a real-time application for responding to an individual scoring request on demand. __ 32. Select Deployment type as Scoring Only. __ 33. Set the Scoring node as Table. __ 34. Select Deploy as stream, as shown in Figure 5-32 on page 51.

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Figure 5-32 Deployment dialog

__ 35. Click Store. __ 36. Go to your Group Folder, which is defined in your worksheet. See Figure 5-33 on page 52.

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Figure 5-33 Store model stream into Repository

__ 37. Name the stream the Modeler Stream Name from your worksheet. __ 38. Click Store. __ 39. Click OK to complete the model deployment. Congratulations, you have completed the lab. __ 40. Select File. What you did in this section: 򐂰 In this section, you successfully created a scoring workflow that uses your newly created model. 򐂰 You deployed the scoring workflow to your analytics platform to be ready for operational application. 򐂰 This model will be used in the next lab.

41.Select Exit. 42.Click Exit. You do not need to save the model because you deployed it to the repository.

5.6 Summary You have seen how you can easily access data and build a good model, and then deploy it to be ready for use in operational application in the enterprise.

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Chapter 6.

Configure the risk assessment in SPSS Decision Management The lab guides you through a structured decision making with the seven steps of SPSS Decision Management (DM) to combine decision with advanced analytics.

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6.1 Introduction to SPSS Decision Management The end goal of advanced analytics is to be able to deploy and apply analytics insights at the very point of impact. In our lab scenario of assessing online loan applications, it means that the retail bank is able to gather all available intelligence to make decisions in real time. There can be potentially thousands of incoming online applications across different types of loans and loan amounts. The bank needs to make a decision on whether to approve or reject an individual loan application in a short time. These decisions are operational and repeatable in nature and may require rules allowing rapid reaction to any market situation. The data for assessing a loan comes from a variety of sources, which may be used to augment the existing risk metrics that the bank uses. An example is FICO scores, with their own data-driven analytics and rules. To handle such challenges, IBM SPSS Decision Management (SPSS DM) enables the automation of such high volume, repeatable decision-making by combining business analytics, business rules, optimization, and data management. SPSS DM drives the power of analytics into optimized decisions in the hand of business by combining analytics results with business knowledge. By providing completely configurable templates such as risk assessment, SPSS DM provides a business tool in a language that the business can understand.

6.1.1 Prerequisites 򐂰 You should have access to the client workstation where you will be performing the lab exercise. Check with your lab facilitator to get access. 򐂰 You should have received your team number from the lab facilitator. You will be using this number during the lab to determine which user ID and which CICS region to use, among other things. 򐂰 You should have already completed the modeling lab.

6.1.2 Create your SPSS Decision Management project This section is to configure a Decision Management specification that combines business rules and predictive models that are created in the previous lab to arrive at an optimal recommendation for incoming loan applications.

Log on to SPSS Decision Management The first thing to do is to log on to SPSS Decision Management. From Decision Management, you then perform the steps required to take an SPSS Modeler Model and business rules to create a new stream: __ 1. Go to the SPSS Decision Management by entering the Decision Management URL from your worksheet. Note: The URL is case-sensitive. __ 2. You should now see a login page very similar to Figure 6-1 on page 55.

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Figure 6-1 SPSS Decision Management login page

__ 3. Enter the DM User as mentioned in your worksheet. __ 4. Enter the DM Password as mentioned in your worksheet. The launch page appears to display configurable solution templates, which are shown in Figure 6-2. SPSS provides a number of vertical configurable solution templates. In this lab, we use the template for assessing credit risk and recommending the best action for incoming loan applications.

Figure 6-2 SPSS Decision Management launch page

On the IBM SPSS Decision Management for Risk Assessment template (shown in Figure 6-3 on page 56) perform the following steps: __ 5. On the drop-down box, select New. __ 6. Click Go.

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Figure 6-3 shows the SPSS Decision Management for Risk Assessment portal.

Figure 6-3 SPSS Decision Management for Risk Assessment portal

A flowchart appears as shown in Figure 6-4. The application template provides a step-by-step workflow, as represented by the icons on the home page. Click any icon to jump to that step.

Figure 6-4 Steps in Risk Assessment Configuration

Figure 6-5 provides a summary of steps in a structured decision making for configuring risk assessment application.

Specify rules that include or exclude cases from processing by the application

Specify how the results of the models and rules should be combined to make the final decision

Specify links to reports stored in the repository

Check the validity of the project, and indicate the real time environment that the project should be deployed to

Define the project data sources for analysis, simulation and testing and scoring. Specify the range of possible actions that can be returned by the application, as well as the models and rules that determine which actions apply

Figure 6-5 Structured decision making steps in the SPSS DM configurable solution template

Step 1: Connect to data Take the following steps to connect to the data. __ 1. Click the Data icon to connect to data source. See Figure 6-6 on page 57.

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Figure 6-6 Connect to data

The Data section opens for entering details, as shown in Figure 6-7.

Project Data Model: De fines the fields required by the application. All other data sets are mapped relative to this source

Project Data Sources: Lists the data sources available in the current project My Data Sources: Lists data sources you have want to r e-use across projects 9

Figure 6-7 Data tab

You use the scoring data view that includes all data elements that are specifically required for operational risk assessment scoring. Follow these steps: __ 2. Under Project Data Model, use the drop-down menu to select Add a data source.... __ 3. Type the DataSource name to RiskDataSource. __ 4. Select the radio box Enterprise View for the data source type. __ 5. Next to Application View, click the Browse button. __ 6. Navigate into the Individual_Risk_Assessment folder by clicking it. __ 7. Select Risk_App_View. Risk_App_View is the application view that includes all data elements that are required for Risk Assessment application. We have already defined this data view for expediency. __ 8. Click Open. __ 9. Select the Table Risk_Scoring_Data.

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Risk_Scoring_Data is the Enterprise View table that specifically includes all data elements that are required for the operational scoring in this scenario. __ 10. Select the Data Provider Risk_DPD_OP. Risk_DPD_OP is the data provider that populates all required data elements for the operational scoring in this scenario. __ 11. Check that the Environment field is set to Operational. __ 12. Expand the Specify Input Fields toggle. __ 13. Check that all the fields are selected. __ 14. The configuration should look similar to Figure 6-8. __ 15. Click Save. __ 16. Click the Save icon, in the toolbar at the top. If you have not done this already, save the project in your Group Folder, which can be found in your worksheet. Call the project the Decision Management Project Name that is specified in the worksheet.

Figure 6-8 Data source configuration

__ 17. Check Mark as Done and click the blue arrow to go to the next step.

Step 2: Global Selections The Global Selections dialog opens, as shown in Figure 6-9 on page 59.

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Figure 6-9 Global Selections dialog

This section allows business users to define the decision scope using Global Selections. The user sets the ground rules for the type of decision that will be rendered. At this point, exclusion or inclusion rules can be defined. For example, the user can include only those loan applications that are less than $500 K US. In this lab, we do not define such rules at this step, for simplicity. __ 1. Check Mark as Done and click the blue forward arrow to go to the next step.

Step 3: Define outcomes In this step, you define three possible actions for incoming transactions: approve, reject, or investigate. The Define dialog opens as shown in Figure 6-10.

Figure 6-10 Define dialog

This section allows business users to define desired outcomes. The result of every decision is an outcome. The business defines what outcomes are expected in given conditions. In this lab, you define three operational decision outcomes for the application: approve, reject, or investigate. __ 1. Look in the left panel. You see a panel that is used for defining decision outcomes, as shown in Figure 6-11 on page 60.

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Figure 6-11 Define Decision Outcomes

__ 2. Click My Action. __ 3. Click the triangle button. __ 4. Select Duplicate, as shown in Figure 6-12. __ 5. Click the triangle button again. __ 6. Select Duplicate again, as shown in Figure 6-12.

Figure 6-12 Duplicate actions

__ 7. You now have three actions, but you have to rename them. To rename each action: • • •

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Click My Action to select it. Click the triangle button. Select Rename.

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Figure 6-13 shows all three decision outcomes. .

Figure 6-13 All three outcomes are now created

__ 8. Click the save icon in the top right. If you have not done this already, save the project in your Group Folder, which can be found in your worksheet. Call the project the Decision Management Project Name that is specified in the worksheet.

Step 4: Define operational decisions with rules and models In this step, you allocate actions with rules and predictive models. The risk analyst configures the decision space by indicating what actions each loan application will be routed to. __ 1. Click to select My Application Request, as shown in Figure 6-14.

Figure 6-14 Define operational decisions with rules and models

In this lab, you will use different techniques: 򐂰 Allocate Action Using Segment Rules 򐂰 Use Rules to Decide Which Action is Triggered 򐂰 Use a Model to Decide Which Action is Triggered In steps 2 on page 62 through 11 on page 63, you allocate actions by Segment Rules, which are high-value rules that will allocate an applicant to a particular action immediately and bypass any other allocation.

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__ 2. Expand Allocate Action Using Segment Rules to open the details section, as shown in Figure 6-15.

Figure 6-15 Allocate Action Using Segment Rules section

Instead of defining rules from scratch, we use the predefined rules for expediency. In steps 3 through 8 on page 63, you define the Low_Values rule that seemed to be highly indicative of risk. If the applicant fits that rule, the loan will be rejected. This allocation provides the risk analyst with total control over his own risk-based processes. __ 3. Click Find an existing rule. __ 4. Select Low_Values.rul from the repository, as shown in Figure 6-16. __ 5. Click Open.

Figure 6-16 Retrieve existing rules from the repository

__ 6. Select the Allocate To drop-down list of Low_Values to select the action, Reject. This rule means that if the applicant has a low education level (<1) and has a maximum overdue number greater than 3, the assessment system automatically rejects the loan. Note: The rules that are used in the lab are made up for illustrating the technology with no reflection on actual bank rules. You can change data attributes and values to create your own custom rule.

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__ 7. Click Low_Values to open the rule preview. This allows you to see the rule in more detail. See Figure 6-17.

Figure 6-17 Low_Values rule preview

__ 8. Click Close. In steps 9 through 11, you define the Small_Loan rule that automatically approves the loan application due to the small amount of the loan. __ 9. Similarly, select Small_Loan.rul from the repository by using the previous steps. The Small_Loan.rul means that if the applicant has a loan request of less than 500, automatically approve the loan. __ 10. This time, set the Allocate to drop-down list of Small_Loan to Approve. You should now have two allocation rules, as shown in Figure 6-18. If you are running ahead, spend some time trying to define your own allocation rules.

Figure 6-18 Allocation rules

__ 11. Close the Allocate Action Using Segment Rules section. In steps 12 through 22 on page 66, you allocate actions by aggregating scores. A score is assigned to each business rule. The sum of these scores will be compared against the pre-determined thresholds for action allocation. __ 12. Expand Use Rules to Decide Which Action is Triggered to open the details section, as shown in Figure 6-19 on page 64.

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Figure 6-19 Use Rules to Decide Which Action is Triggered section

Again, instead of defining rules from scratch, we use the predefined rules for expediency: __ 13. Click Find an existing rule. __ 14. Select to retrieve the Scoring_Rules.rul from the repository. __ 15. Click Open. __ 16. When the dialog asks if the Scoring_Rules.rul is to be a Convert or Replace, select Convert. The section now displays the imported rule set that will be used to add additional points to the application’s risk score. You now configure the risk points for each rule. Figure 6-20 on page 65 is a suggestion of how the risk points could be associated with the rules. In this step, you can experiment with assigning the risk points with your own scores for the rules. The allocation rules are based on common risk metrics such as credit history, debt-income ratio, and credit utilization. Thresholds have been established for these common metrics at the corporate level and for each key threshold, the risk analyst assigns points. In this example, the higher the point value, the better the risk.

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Figure 6-20 Example of Risk point allocation

You now have a score that is generated from the rules. Next, you need to associate an action with that score. Steps 17 through 21 are to compare the sum of scores against the pre-determined thresholds. The higher that the sum of scores is, the higher credit-worthy the applicant is and hence, the more likely the application is to be approved. __ 17. Under the rules, click the Add Action button twice. __ 18. Set line 1 so that the sum of the points >= 400; the action is to Approve. __ 19. Set line 2 so that the sum of points >= 200; the action is Investigate. __ 20. For the remaining (line 3), the action is Reject. __ 21. Click the Save icon (the floppy disk) in the top right to save the project. The section should now look like Figure 6-21 on page 66.

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Figure 6-21 Use rules to decide which action is triggered

__ 22. Close the Use Rules to Decide Which Action is Triggered section. You use the model that you created in the modeler module. In steps 1 through 11, you allocate actions by comparing the model propensity with the pre-determined threshold. The higher the model propensity that is close to one is, the more likely the transaction is to be approved: __ 1. Expand the Use a Model to Decide Which Action is Triggered section. __ 2. Click Find a model. __ 3. Browse into your group folder (specified in the worksheet) and select the model stream that you created earlier, called Individual_Risk_Assessment.str. __ 4. Click Open. __ 5. Click the drop-down menu under Measure. __ 6. Select Propensity. __ 7. Underneath the model, click Add Action two times. __ 8. Set line 3 (the remainder) to 0 and the action to Reject. __ 9. Set line 2 to 0.2 and the action to Investigate. __ 10. Set line 1 to 0.8 and the action to Approve. __ 11. Save the project. The section should now look like Figure 6-22 on page 67. While rules capture organizational history and are based on things that the risk analyst has measured and observed, a model has been configured that predicts, in real time, whether a given applicant is approvable and calculates their approvable propensity. The propensity is used to allocate an action. Of those 66

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applicants, the model indicates a high probability to be approvable and receive a more favorable outcome than those that have a lower probability to be approved.

Figure 6-22 Use a model to decide which action is triggered

After you have defined the operational decisions with rules and models, you can simulate on your new configuration of Allocation, Model, and Rule for impact analysis. Business users effectively use this facility to conduct “What-If” simulation until the rule and model configuration is finalized. Follow these steps iteratively until the user is satisfied with the results: __ 12. Click Simulate and then click Run. See the Simulation dialog, as shown in Figure 6-23. Simulation within the define step shows how the allocations, rules, and models trigger across the population. At the Application Request tab, you see the total number of cases used in this simulation.

Figure 6-23 Total cases for simulation Chapter 6. Configure the risk assessment in SPSS Decision Management

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Step 13 allows the risk analyst to compare the allocation results side by side. __ 13. Select the Action tab to see the number of loan applications that fall into each of the outcomes that are based on Allocation, Model, and Rule, as shown in Figure 6-24.

Figure 6-24 Action results

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Steps 14 and 15 on page 70, allow the analyst to drill into the rules and the allocations by segment rules: __ 14. Select the Rule tab to see the number of loan applications that fall into each Rule, as shown in Figure 6-25.

Figure 6-25 Rule tab

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__ 15. Select the Allocation tab to see the number of loan applications that fall into each Allocation Rule, as shown in Figure 6-26.

Figure 6-26 Allocation tab

When you have finished with simulation, close the simulation window and move on to the next section.

6.1.3 Combine At the combine stage, the risk analyst indicates how the model outcome and the rule-based outcome should adjudicate. Simulation allows them to visualize the results and understand how many applicants, given their project data, would fit each of their configured actions. The next steps are to specify how rules and models are combined together to determine the recommended action for each application. You can then use this to simulate the end result: __ 1. Click the Combine tab. __ 2. The first thing that we do is to assign colors to the values. Click the white box that is next to Reject. __ 3. A color palette should appear, as in Figure 6-27 on page 71. Click red.

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Figure 6-27 Action color palette

__ 4. Repeat for yellow for Investigate. __ 5. Repeat for green for Approve. The table should now look like Figure 6-28. The actions are color coded, which is a visual display of how your rules and models are combined together.

Figure 6-28 Application Request matrix

Step 6 allows the risk analyst to adjust the combination, or go back and adjust the rules and models, until the results are consistent with the organization’s risk-tolerance and resource realities. __ 6. At this step, click “What-if”, then click Run. This runs a simulation on the combinations of rules and models. Changing the decision allocations in this table and using the simulation will help you arrive at the optimal configuration. We suggest the settings that are shown in Figure 6-29.

Figure 6-29 Customized Application Request matrix

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__ 7. To change the decision allocations, click the triangle on the decision and select which action that you want to be performed. See Figure 6-30.

Figure 6-30 Selecting the decision combination

When you have finished experimenting, close the simulation box.

6.1.4 Deploy After actions, rules and models are defined and the analyst has determined how rules and models interact. The project can be deployed. Steps 1 through 10 are to deploy the configuration to the repository. Finally, you need to validate your current application processing project configuration and mark it as ready to be deployed: __ 1. Click Save. __ 2. Click the Deploy tab. __ 3. Set Deploy as to Deploy. __ 4. Click Validate. __ 5. Check that the validation message reads: The application workspace is valid for deployment. __ 6. Click OK. __ 7. Click Deploy. __ 8. If asked, click Move. __ 9. Check that the deployment message reads: The application workspace has been successfully deployed. __ 10. Click OK. Congratulations. You are now finished with SPSS Decision Management; you can close that window.

6.1.5 Summary In this module, you took the analytics model that you created in the first lab and enriched the outcome with business rules to arrive at an optimized configuration for the risk assessment through simulation. After you configured the data, rules, and combination, you then deployed the SPSS DM stream to the repository to be used by the operational processes. In the next lab, you use this DM stream to create a scoring service, which can be called in real time. 72

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Chapter 7.

Configuration of the risk assessment application for real-time scoring In this part of the lab, you create a real-time scoring engine that is based on the configured SPSS Decision Management. Then, a CICS application can call a web service to request scoring in operational systems, informing decisions in either real time or batch. A risk analyst can change the parameters or configuration at any time and see results immediately. This is particularly useful if a new risk pattern has emerged and the risk analyst needs to quickly adjust to the new risk reality.

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7.1 Introduction The lab guides you through setting up, testing, and invoking the scoring service using an SPSS Decision Management stream and CICS Transaction Server. At the end of this lab, you will know how to perform the following actions: 1. Configure SPSS Collaboration and Deployment Services for scoring 2. Use a CICS transaction to call the scoring service

7.1.1 Prerequisites 򐂰 You should have access to the client where you will be performing the lab exercise. Check with your lab facilitator for access. 򐂰 You should receive your team number from the lab facilitator. You will be using this number during lab to determine some of the worksheet parameters. 򐂰 You should have already completed the analytics modeling with IBM SPSS Modeler and configured the risk assessment in SPSS Decision Management modules.

7.1.2 Create a scoring configuration for the Decision Manager stream Before a stream can be used for real-time scoring, you must define some supplemental information. The scoring configuration allows you to define which parameters, outputs, identification, real-time data provider, logging, and cache that you want the scoring to use. This allows for a single model to be used in a variety of scoring situations. In the next steps, you create a scoring configuration for the Decision Manager stream that you created in the last module. The scoring configuration allows you to call the SPSS Collaboration and Deployment Services scoring service with the LOANID and AMOUNT parameters. The scoring configuration then uses the provided Real-Time Data Provider Definition to populate the rest of the scoring parameters with data from the database. We then specify the scoring to return the advised action and the risk score: __ 1. Open IBM SPSS Collaboration and Deployment Services  Deployment Manager 4.2.1 from the Windows Start menu. __ 2. Right-click CnDS. __ 3. Click Log on as.... __ 4. Use the CnDS User and CnDS Password in your worksheet to authenticate. __ 5. Expand CnDS. __ 6. Expand Content Repository. __ 7. Expand your Group Folder specified to you in your worksheet. You should be able to see the SPSS Decision Management stream that you just deployed, with the name Individual_Risk_Assessment_DM.str. __ 8. Right-click the name and select Configure Scoring, as shown in Figure 7-1 on page 75.

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Figure 7-1 Configure Scoring option

__ 9. Enter the Name that is specified as the Scoring ID in the worksheet. __ 10. Leave the Label as LATEST. __ 11. Click Next. __ 12. Leave the Enable Interactive Score check box unchecked. __ 13. Click Next. __ 14. In the Data Provider Settings dialog: • • • • •

Check Use Data Provider Select the Data Provider: RiskRTDPD Set the Label to LATEST Select the Table: Risk_Scoring_Data Select the Key: Scoring_Key

__ 15. The configuration should look similar to Figure 7-2 on page 76.

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Figure 7-2 Data Provider settings

__ 16. Click Next. __ 17. In the Input Data Order dialog: •

Move LOANID to the top of the list of inputs by selecting it and clicking Up.

•

Move AMOUNT under LOANID, if it is not already there.

These are the most important inputs, as well as the only inputs that we include as real-time data. The data provider uses the LOANID to get the data to fill the rest of the columns. See Figure 7-3 on page 77 for the configure scoring model input data order.

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Figure 7-3 Configure scoring model input data order

__ 18. Click Next. __ 19. In the Input Data Returned Settings dialog, leave the Return model inputs in response check box unchecked. __ 20. Click Next. __ 21. The only two fields that we want to return in this Proof of Technology (PoT) are Aggregate-Value and Action. Therefore, in the Output Data Returned Settings dialog, check these two fields, as shown in Figure 7-4 on page 78. Shortcut: Unchecking Model Outputs automatically unchecks all of the fields.

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Figure 7-4 Configure scoring model output data

__ 22. Click Finish.

7.1.3 Use SPSS Collaboration and Deployment Services portal to test the scoring configuration Now that you have defined the scoring configuration, you can test that you can use the model for real-time scoring using the deployment portal. The SPSS Collaboration and Deployment Services portal interface allows for all users with sufficient permissions to access the artifacts, logs, and results stored in the repository, as well as run jobs to generate scores, all through a thin client interface. In the next steps, we use the thin client to verify that we can use the scoring configuration to get scores from the Decision Manager stream. __ 1. In your VM, open the deployment portal at http://<Scoring Address>:<Scoring Port>/peb. Where Scoring Address and Scoring Port are specified in your worksheet. __ 2. Log in to the Collaboration and Deployment Services Deployment Portal with the CnDS User and CnDS Password in your worksheet. __ 3. Click the Content Repository tab. __ 4. Click your Group Folder, which is specified in your worksheet. __ 5. Click your Decision Management stream, which is specified as the Decision Management Project Name in your worksheet. __ 6. If you are prompted for your scoring configuration, select the one that you just created. You used the name specified by the Scoring ID variable in your worksheet. __ 7. You are now able to see the inputs that are required for the scoring. __ 8. Enter in a LOANID 1 - 9.

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__ 9. Check the box in front of AMOUNT. By doing this, you are informing the scoring service that you want to use this value over the value in the database. __ 10. Enter an amount.

Figure 7-5 Entering scoring parameters

__ 11. Click Score. This button can be found at the bottom of all the inputs. __ 12. You should see a result at the very bottom of the page, as shown in Figure 7-6.

Figure 7-6 Successful scoring result

7.1.4 Call the scoring service from a CICS transaction Introduction Now that you have a scoring service that is configured and tested, the next step is to call the service in real time from a CICS transaction. There are many ways to create a CICS Web service requestor program. For this lab, the requestor application has already been written using the CICS Web services assistant tool. The creation of the CICS Web services requestor involved the following steps (illustrated in Figure 7-7 on page 80): 1. Used SPSS Modeler to create a modeler stream and generate a predictive model from some business data that is stored in a DB2 database. Defined a scoring branch on the modeler stream. 2. Deployed the modeler stream to SPSS Collaboration and Deployment Services specifying the scoring node and parameters. We then enhanced the stream by using SPSS Decision Management. 3. Defined a scoring configuration in SPSS Collaboration and Deployment Services for the deployed stream. This allows the predictive model to be used for scoring via the SPSS Collaboration and Deployment Services scoring service.

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4. Obtained the Web Services Description Language (WSDL) and schema documents, which define the generic SPSS Collaboration and Deployment Services scoring service. Used the CICS Web services assistant tool, DFHWS2LS, to generate a high-level language data structure and a web service binding file from a web service description. 5. Developed a CICS COBOL service requester application using the code that is generated by the DFHWS2LS tool to call the getScore operation on the scoring service. Note: Only steps 4 and 5 would be required. Steps 1 - 3 have already been described in the previous models that were covered in the prerequisites section.

1. Generate Modeler stream with scoring branch

1 SPSS Modeler Client

2. Specify scoring node and parameters, then Deploy to SPSS Collaboration and Deployment Services

2 SPSS Collaboration and Deployment Services Deployment Manager 4 CICS TS

5

3 3. Define scoring configurati on from deployed stream

4. Generate a CICS web service client/requester from scoring service WSDL

5. Call the scoring service from CICS

Figure 7-7 Creating the CICS scoring service requestor application

This application has already been deployed for you, so in the last part of this lab you will perform the following steps: 1. 2. 3. 4.

Log on to the CICS region on the mainframe using a 3270 terminal Invoke the scoring application Configure the application to point at your scoring service Execute the scoring in real time from the CICS transaction

Log on to the CICS region on the mainframe using a 3270 terminal The first thing that you need to do is open a 3270 terminal session to the mainframe. You will use this to log onto the CICS region so that you can invoke a transaction to call the scoring service. Your client workstation needs IBM Personal Communications for this: __ 1. Open IBM Personal Communications and double-click wtsc90. See Figure 7-8 on page 81.

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Figure 7-8 Link parameter setup panel

__ 2. Click OK. You should see a logon window that is similar to Figure 7-9.

Figure 7-9 ITSO logon window

__ 3. To log in to the CICS region, type in the CICS Region Name from your worksheet. __ 4. Press the right control button (Ctrl) to enter the command. __ 5. Enter the z/OS user ID and z/OS Password from your worksheet, as shown in Figure 7-10 on page 82. __ 6. Press the right control button (Ctrl) to enter the logon credentials.

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__ 7. You should see a message such as DFHCE3549 Sign-on is complete, which informs you that your logon was successful.

Figure 7-10 Signon to CICS window

Invoke the scoring transaction The scoring transaction provides a CICS 3270 terminal user interface to collect real-time input for the scoring. This is one example of how to consummate a CICS transaction using real-time scoring. To start the scoring application, take the following steps: __ 1. Press the pause button to clear the display. __ 2. In the top left corner, type the transaction name. This is the Scoring Transaction variable in your worksheet. See Figure 7-11 on page 83 for an example.

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Figure 7-11 Entering the scoring transaction

__ 3. Press the right control button (Ctrl) to submit the transaction to CICS. __ 4. You should now see the scoring application CICS user interface (UI). This will look similar to Figure 7-12.

Figure 7-12 CICS scoring 3270 user interface

Configure the application to point at your scoring service There is a configuration part at the bottom of the application window. This allows for you to point the CICS transaction at different SPSS Collaboration and Deployment Service servers and use the scoring configuration that you just made.

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__ 1. Set the Scoring ID to the ID of the scoring configuration that you made. This is the same as the Scoring ID variable in your worksheet. __ 2. Set the Machine Addr to the Scoring Address variable in your worksheet. __ 3. Set the Port to the Scoring Port variable in your worksheet.

Execute the scoring in real time from the CICS transaction Now that the scoring transaction has the correct scoring parameters, you can call your scoring configuration in real time from the CICS application. This is done by following these steps: __ 1. Enter a Loan ID. This can be a number between 1 and 9. __ 2. Enter an Amount. __ 3. Press the right control button (Ctrl) to submit the inputs for scoring. __ 4. You now can see that a scoring algorithm has run and returned a risk score and a recommend action. Note: The input fields add leading zeros to the data values, such as 000000003.

Figure 7-13 Successful scoring execution

Note: If you see an error message pop-up window in the application, check your configuration, your inputs, and that your scoring ID name is in capital letters. For example, NON-ZERO RC FROM INVOKE SERVICE.

7.1.5 Summary In this module, you created a scoring configuration that allows you to use the Decision Management stream that you created to be called in real time. In this example, the call from real time is coming from a CICS transaction with real-time data provided on a CICS 3270 terminal user interface. 84

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A

Appendix A.

Additional material This book refers to additional material that can be downloaded from the Internet as described in the following sections.

Locating the Web material The Web material associated with this book is available in softcopy on the Internet from the IBM Redbooks Web server. Point your Web browser at: ftp://www.redbooks.ibm.com/redbooks/SG248153 Alternatively, you can go to the IBM Redbooks website at: ibm.com/redbooks Select the Additional materials and open the directory that corresponds with the IBM Redbooks form number, SG248153.

Using the Web material The additional Web material that accompanies this book includes the following files: File name 8153 POT.zip

Description Zipped Presentations and PDFs

System requirements for downloading the Web material The Web material requires the following system configuration: Hard disk space: Operating System:

7 MB minimum Windows

© Copyright IBM Corp. 2013. All rights reserved.

85

Downloading and extracting the Web material Create a subdirectory (folder) on your workstation, and extract the contents of the Web material.zip file into this folder.

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Related publications The publications listed in this section are considered particularly suitable for a more detailed discussion of the topics covered in this book.

IBM Redbooks The following IBM Redbooks publications provide more information about the topic in this document. Some publications referenced in this list might be available in softcopy only. 򐂰 DB2 10 for z/OS Technical Overview, SG24-7892 򐂰 Using zEnterprise for Smart Analytics: Volume 2 Implementation, SG24-8008 You can search for, view, download, or order these documents and other Redbooks, Redpapers, Web Docs, draft, and additional materials, at the following website: ibm.com/redbooks

Other publications These publications are also relevant as further information sources: 򐂰 DB2 10 for z/OS Installation and Migration Guide, GC19-2974 򐂰 DB2 for z/OS Application Programming Topics, SG24-6300 򐂰 IBM SPSS Modeler Server Scoring Adapter for DB2 on z/OS License Information, GC19-3721 򐂰 IBM SPSS Modeler Server Scoring Adapter for DB2 on z/OS Program Directory, GI10-8919

Online resources These websites are also relevant as further information sources: 򐂰 IBM Business Analytics on System z http://www.ibm.com/software/os/systemz/badw 򐂰 IBM DB2 Accessories Suite for z/OS - Software http://www.ibm.com/software/data/db2imstools/db2tools/accessories-suite/ 򐂰 IBM SPSS software http://www.ibm.com/software/analytics/spss

© Copyright IBM Corp. 2013. All rights reserved.

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Help from IBM IBM Support and downloads ibm.com/support IBM Global Services ibm.com/services

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Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

(0.2”spine) 0.17”<->0.473” 90<->249 pages

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Risk Scoring for a Loan Application on IBM System z: Running IBM SPSS Real-Time Analytics

Back cover

®

Risk Scoring for a Loan Application on IBM System z Running IBM SPSS Real-Time Analytics ®

Using IBM SPSS Modeler for Analytics modeling Configuring risk assessment in SPSS Decision Management Real-time scoring using a System z host

When ricocheting a solution that involves analytics, the mainframe might not be the first platform that comes to mind. However, the IBM System z group has developed some innovative solutions that include the well-respected mainframe benefits. This book describes a workshop that demonstrates the use of real-time advanced analytics for enhancing core banking decisions using a loan origination example. The workshop is a live hands-on experience of the entire process from analytics modeling to deployment of real-time scoring services for use on IBM z/OS. In this IBM Redbooks publication, we include a facilitator guide chapter as well as a participant guide chapter. The facilitator guide includes information about the preparation, such as the needed material, resources, and steps to set up and run this workshop. The participant guide shows step-by-step the tasks for a successful learning experience. The goal of the first hands-on exercise is to learn how to use IBM SPSS Modeler for Analytics modeling. This provides the basis for the next exercise “Configuring risk assessment in SPSS Decision Management”. In the third exercise, the participant experiences how real-time scoring can be implemented on a System z. This publication is written for consultants, IT architects, and IT administrators who want to become familiar with SPSS and analytics solutions on the System z.

INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION

BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.

For more information: ibm.com/redbooks SG24-8153-00

ISBN 0738438847